class_docstr: A wrapper for torch.optim.Optimizer objects that helps with managing dynamically generated parameters.
  
  :param optim_constructor: a torch.optim.Optimizer
  :param optim_args: a dictionary of learning arguments for the optimizer or a callable that returns
      such dictionaries
  :param clip_args: a dictionary of clip_norm and/or clip_value args or a callable that returns
      such dictionaries
docstr: Set the state associated with all the optimizers using the state obtained
  from a previous call to get_state()
docstr_tok: ["Set","the","state","associated","with","all","the","optimizers","using","the","state","obtained","from","a","previous","call","to","get_state","(",")"]
code: def set_state(self, state_dict: Dict) -> None:
      """
      Set the state associated with all the optimizers using the state obtained
      from a previous call to get_state()
      """
      self._state_waiting_to_be_consumed.update(state_dict)
  
imports: import inspect
  from typing import Any, Callable, Dict, Iterable, List, Optional, Type, Union, ValuesView
  import torch
  from torch import Tensor
  from torch.nn.utils import clip_grad_norm_, clip_grad_value_
  from torch.optim import Optimizer
  import pyro
  from pyro.optim.adagrad_rmsprop import AdagradRMSProp
  from pyro.optim.clipped_adam import ClippedAdam
  from pyro.optim.dct_adam import DCTAdam
  from pyro.params.param_store import module_from_param_with_module_name, normalize_param_name, user_param_name
label_desc: Use image node_id 4 for calling the PyroOptim obj's underlying member method code with example usage: obj.set_state(state_dict) without return types